code: def _decoder_layer(c: TransformerConfigs):
      """
      Decoder layer
      """
      return TransformerLayer(
          d_model=c.d_model,
          self_attn=c.decoder_attn,
          src_attn=c.decoder_mem_attn,
          feed_forward=copy.deepcopy(c.ffn.ffn),
          dropout_prob=c.dropout,
      )
  
imports: import copy
  import torch.nn
  from labml.configs import BaseConfigs, option, calculate, aggregate
  from labml_helpers.module import Module
  from .feed_forward import FeedForward
  from .mha import MultiHeadAttention
  from .models import EmbeddingsWithPositionalEncoding, EmbeddingsWithLearnedPositionalEncoding, TransformerLayer, Encoder, Decoder, Generator, EncoderDecoder
label_desc: Use image node_id 8 for calling a global function with example usage: _decoder_layer(c) and returns: TransformerLayer